The Graph Traversal Pattern

Rodriguez, Marko A.; Neubauer, Peter

doi:10.4018/978-1-61350-053-8.ch002

Cited by 55 publications

(61 citation statements)

References 23 publications

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“…A property graph [10] can be formally defined as = ( , , , ), where is a set of nodes, ⊆ { × } is a multiset of directed edges, : → Σ is a label function, Σ is a set of edge labels, and : ( ∪ )× → is a key-value mapping function that maps property keys of nodes and edges to their values, where is a key set and is the set of property values. What makes a property graph more expressive is that it contains multiple key-value maps on every node and edge.…”

Section: Property Graph Theorymentioning

confidence: 99%

Assisting in Auditing of Buffer Overflow Vulnerabilities via Machine Learning

Meng

Feng

Bin

et al. 2017

Mathematical Problems in Engineering

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Buffer overflow vulnerability is a kind of consequence in which programmers' intentions are not implemented correctly. In this paper, a static analysis method based on machine learning is proposed to assist in auditing buffer overflow vulnerabilities. First, an extended code property graph is constructed from the source code to extract seven kinds of static attributes, which are used to describe buffer properties. After embedding these attributes into a vector space, five frequently used machine learning algorithms are employed to classify the functions into suspicious vulnerable functions and secure ones. The five classifiers reached an average recall of 83.5%, average true negative rate of 85.9%, a best recall of 96.6%, and a best true negative rate of 91.4%. Due to the imbalance of the training samples, the average precision of the classifiers is 68.9% and the average 1 score is 75.2%. When the classifiers were applied to a new program, our method could reduce the false positive to 1/12 compared to Flawfinder.

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Section: Property Graph Theorymentioning

confidence: 99%

Assisting in Auditing of Buffer Overflow Vulnerabilities via Machine Learning

Meng

Feng

Bin

et al. 2017

Mathematical Problems in Engineering

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“…The notation presented here is based on [14] and [26] with minor adaptations to simplify presentation.…”

Section: Labeled Graphsmentioning

confidence: 99%

Evaluation of Optimization Strategies for Incremental Graph Queries

Szárnyas

Maginecz²,

Varró

2017

Period. Polytech. Elec. Eng. Comp. Sci.

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Keywords graph queries, relational algebra, query optimization IntroductionThe key components of Big Data are often defined as variety, velocity and volume [28] of data. Applications operating on continuously changing graphs are a prime example: the semi-structured graph-like nature introduces a high variety, changes happen at high velocity, and datasets are often high-volume. Such applications include fraud detection in financial transactions [27], validation of engineering models [3], and static analysis of source code repositories [35]. These use cases provide a set of complex queries that need to be evaluated continuously on each change of the underlying graph.Traditional approaches need to reevaluate each query upon each change, which often takes minutes on a large dataset. In contrast, incremental query evaluation caches interim results, hence it only requires reevaluation on a small fragment of the dataset impacted by the change. This leads to significant speedup for large and continuously changing data. Although several approaches exist for incremental query evaluation [9,20] in the context of expert systems, incremental query evaluation is not in widespread use in graph databases.In order to predict query performance at runtime, relational databases synthesize and evaluate different query plans which impose a certain ordering on relational algebraic operations prescribed by the query. Optimizing query plans is a challenging task, since a wide variety of query plans may exist even for simple queries with different costs. Database engines use heuristics-based optimization techniques and evaluate a cost function for the different query plans [10].Query plans have been adapted for graph query engines using a local-search based query evaluation strategy where it is called the search plan. Optimization techniques may exploit the type and multiplicity information defined in the graph schema (or metamodel) [29,22] or rely upon runtime statistics of the instance graph [11,38,39].In case of incremental graph query engines, the structure and the content of caches have the most significant impact on query performance. Therefore, optimization is directed to reduce execution time and memory consumption imposed by a complex network of caches [37].

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“…A graph database [17] represents vertices and edges as a graph structure instead of storing data in separated tables. It is designed specifically for graph operations.…”

Section: The Graph Databasementioning

confidence: 99%

Large scale cohesive subgraphs discovery for social network visual analysis

Zhao

Tung

2012

Proc. VLDB Endow.

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Graphs are widely used in large scale social network analysis nowadays. Not only analysts need to focus on cohesive subgraphs to study patterns among social actors, but also normal users are interested in discovering what happening in their neighborhood. However, effectively storing large scale social network and efficiently identifying cohesive subgraphs is challenging. In this work we introduce a novel subgraph concept to capture the cohesion in social interactions, and propose an I/O efficient approach to discover cohesive subgraphs.Besides, we propose an analytic system which allows users to perform intuitive, visual browsing on large scale social networks. Our system stores the network as a social graph in the graph database, retrieves a local cohesive subgraph based on the input keywords, and then hierarchically visualizes the subgraph out on orbital layout, in which more important social actors are located in the center. By summarizing textual interactions between social actors as tag cloud, we provide a way to quickly locate active social communities and their interactions in a unified view.

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The Graph Traversal Pattern

Cited by 55 publications

References 23 publications

Assisting in Auditing of Buffer Overflow Vulnerabilities via Machine Learning

Assisting in Auditing of Buffer Overflow Vulnerabilities via Machine Learning

Evaluation of Optimization Strategies for Incremental Graph Queries

Large scale cohesive subgraphs discovery for social network visual analysis

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